Mining Sequential Patterns from Large Data Sets

Yang Jiong Wang Wei

ISBN 10: 1441937072 / ISBN 13: 9781441937070

Published by Springer, 2010

About this Item

PRINT ON DEMAND pp. 180.

Seller Inventory # 183101639

Bibliographic Details

• Title: Mining Sequential Patterns from Large Data Sets
• Publisher: Springer
• Publication Date: 2010
• Language: English
• ISBN 10: 1441937072
• ISBN 13: 9781441937070
• Binding: Soft cover
• Condition: New

About this title

Synopsis

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. It provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable.

From the Back Cover

The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining.  In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences.  This information has been of great interest for analyzing the sequential data to find its inherent characteristics.  Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces.

To meet the different needs of various applications, several models of sequential patterns have been proposed.   This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. 

Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable.  This book provides an efficient algorithm for mining these patterns.

Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science.